Multi-Vector Model Predictive Control for Permanent Magnet Synchronous Motors with Disturbance Compensation

Targeting the weaknesses of conventional model predictive control (MPC), which fails to suppress disturbances effectively and has significant current pulsations, a multi-vector MPC strategy based on improved active disturbance rejection control (ADRC) is proposed in this paper. To enable voltage vector (VV) combinations applied to inverters to cover any direction with variable amplitude, four VVs are selected based on the sector-based vector selection method and the cost function, with the consideration of reductions in the number of switching transitions and common-mode voltage (CMV). In addition, current slopes are introduced for deadbeat control to achieve duty cycle-based control. Furthermore, to overcome the limitations of the conventional speed loop that cannot consider both rapid response and overshoot, an improved ADRC controller with disturbance compensation is designed to optimize the control performance. Dynamic observation and the feedback compensation of disturbances are implemented by utilizing a cascaded extended state observer (ESO). To alleviate the estimation burden of cascaded ESO, partial disturbances are evaluated and subsequently compensated into the controller. The simulation results demonstrate the feasibility of the proposed multi-vector MPC strategy that enables effective disturbance suppression.

Authors

Institutions

Publication Details

Published
2024-11-26
DOI
https://doi.org/10.23919/icems60997.2024.10921187
Primary Topic
Sensorless Control of Electric Motors
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-Vector Model Predictive Control for Permanent Magnet Synchronous Motors with Disturbance Compensation

Seungjoo Kim, Jialiang Dai, Ju Lee, Zhaoyi Wang et al.
Sensorless Control of Electric Motors
article

Multi-Vector Model Predictive Control for Permanent Magnet Synchronous Motors with Disturbance Compensation

Seungjoo Kim, Jialiang Dai, Ju Lee, Zhaoyi Wang, Jungho Ahn
article en

Abstract

Targeting the weaknesses of conventional model predictive control (MPC), which fails to suppress disturbances effectively and has significant current pulsations, a multi-vector MPC strategy based on improved active disturbance rejection control (ADRC) is proposed in this paper. To enable voltage vector (VV) combinations applied to inverters to cover any direction with variable amplitude, four VVs are selected based on the sector-based vector selection method and the cost function, with the consideration of reductions in the number of switching transitions and common-mode voltage (CMV). In addition, current slopes are introduced for deadbeat control to achieve duty cycle-based control. Furthermore, to overcome the limitations of the conventional speed loop that cannot consider both rapid response and overshoot, an improved ADRC controller with disturbance compensation is designed to optimize the control performance. Dynamic observation and the feedback compensation of disturbances are implemented by utilizing a cascaded extended state observer (ESO). To alleviate the estimation burden of cascaded ESO, partial disturbances are evaluated and subsequently compensated into the controller. The simulation results demonstrate the feasibility of the proposed multi-vector MPC strategy that enables effective disturbance suppression.

Korea Testing Certification (KR), Hanyang University (KR)
Korea Electric Power Corporation
Affordable and clean energy
Openalex Percentile: Top 74%
Sensorless Control of Electric Motors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.